Off-the-shelf localization modules such as Extended Kalman Filter (EKF)-based localization [1]}, Markov localization [2]} and Adaptive Monte-Carlo localization (AMCL) [3]} are based on a Bayesian filtering viewpoint of localization. All these methods begin with a prior (usually uniform) belief of a robot's pose and recursively update the belief as new (uncertain) measurements arrive. The representation of the belief is what fundamentally differentiates each approach. EKF-based methods assume a Gaussian distribution to characterize the belief, while Markov localization [2]} and AMCL [3]} use non-parameteric distributions to characterize the belief (histograms and particles respectively). Consequently, EKF localization lacks the capability to characterize multiple belief hypotheses (owing to the unimodal Gaussian prior), while Markov localization and AMCL suffer computational overhead as the population of the non-parameteric distribution increases.
